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Compilation of a function in a separate step before running the
program code, producing an on-disk binary object which can be distributed
independently. This is the traditional kind of compilation known
in languages such as C, C++ or Fortran.

bytecodePython bytecode

The original form in which Python functions are executed. Python
bytecode describes a stack-machine executing abstract (untyped)
operations using operands from both the function stack and the
execution environment (e.g. global variables).

compile-time constant

An expression whose value Numba can infer and freeze at compile-time.
Global variables and closure variables are compile-time constants.

A feature of compilation in object mode where a loop can be
automatically extracted and compiled in nopython mode. This
allows functions with operations unsupported in nopython mode to see
significant performance improvements if they contain loops with only
nopython-supported operations.

lowering

The act of translating Numba IR into LLVM IR. The term
“lowering” stems from the fact that LLVM IR is low-level and
machine-specific while Numba IR is high-level and abstract.

nopython mode

A Numba compilation mode that generates code that does not access the
Python C API. This compilation mode produces the highest performance
code, but requires that the native types of all values in the function
can be inferred. Unless otherwise instructed,
the @jit decorator will automatically fall back to object
mode if nopython mode cannot be used.

Numba IRNumba intermediate representation

A representation of a piece of Python code which is more amenable
to analysis and transformations than the original Python
bytecode.

object mode

A Numba compilation mode that generates code that handles all values
as Python objects and uses the Python C API to perform all operations
on those objects. Code compiled in object mode will often run
no faster than Python interpreted code, unless the Numba compiler can
take advantage of loop-jitting.

type inference

The process by which Numba determines the specialized types of all
values within a function being compiled. Type inference can fail
if arguments or globals have Python types unknown to Numba, or if
functions are used that are not recognized by Numba. Sucessful
type inference is a prerequisite for compilation in
nopython mode.